library(tidyverse)
library(readxl)
library(showtext)
library(gganimate)
library(gifski)
date_caption <- "13 janvier 2025"
source("tools/themes.R")
musee_frequentation <- read_csv("posts/2025-01-25/data/antibes-frequentation-musees-france.csv")Introduction
Ce poste analyse le nombre de visiteurs dans les musées d’Antibes depuis 2008. Une animation est réalisée pour illustrer la baisse durant le COVID. Puis la reprise à des niveaux records. Les musées concernés sont le Musée Picasso et le Musée de l’Archéologie. Le nombre de visiteurs est agrégé sur ces deux musées.
Résultats
Code
Import
Analysis
# data to draw line geom
dataplot <-
musee_frequentation %>%
filter(ANNEE>2007) %>%
group_by(ANNEE) %>%
summarize(tot = sum(NB_TOTAL_ENTREES))
dataplot# A tibble: 17 × 2
ANNEE tot
<dbl> <dbl>
1 2008 110068
2 2009 140005
3 2010 138800
4 2011 135151
5 2012 132157
6 2013 137711
7 2014 167305
8 2015 150944
9 2016 131244
10 2017 134775
11 2018 124964
12 2019 142284
13 2020 47237
14 2021 78463
15 2022 160544
16 2023 201000
17 2024 197190
# data to draw dot symbolizing the max value at the end of the line
dataplot_max <-
dataplot %>%
filter(ANNEE == max(ANNEE))
dataplot_max# A tibble: 1 × 2
ANNEE tot
<dbl> <dbl>
1 2024 197190
#highlight color for plot
highlight <- "#0E2C48"
highlighttext <- "#4B9E8C"
highlightmain <- "#824B79"Plot
# Static Plot for testing
plot_musee_frequentation <-
dataplot %>%
ggplot(aes(x = ANNEE, y = tot/1000, group = 1)) + #divided to obtain thousands
geom_line(size = 1.5, color = "#0E2C48") +
geom_point(data = dataplot_max, size = 4, color = highlight) +
scale_x_continuous(breaks = c(2014,2020,2023, 2024),
expand = c(0.1, 0.1),
)
plot_musee_frequentation# Animated Plot
# Create a new col to control the speed of the animation
dataplot_slowdown <-
dataplot %>%
mutate(
show_time = case_when( # Adjust show_time for specific years
ANNEE %in% c(2020) ~ 10, # Show these years longer
TRUE ~ 1), # Default speed for others
reveal_time = cumsum(show_time) # Cumulative time for each point
)
# Get thousands instead of individual visitors
dataplot_slowdown$tot <- round(dataplot_slowdown$tot/1000, 0)
# Final Plot
plot_musee_anim <-
dataplot_slowdown %>%
ggplot(aes(x = ANNEE, y = tot, group = 1)) +
geom_line(size = 2, color = highlightmain) +
geom_point(size = 4, color = highlightmain) +
geom_text(aes(label = paste(tot)),
vjust = 0, hjust = -0.3, color = "#3D2358",
fontface = "bold",
family = setfont,
size = 6, show.legend = FALSE) +
geom_text(aes(
x = 2016,
y = max(dataplot_slowdown$tot) - 26,
label = paste(ANNEE)),
vjust = -0.05,
hjust = 0.15,
color = "#946285",
# fontface = "bold",
size = 25, show.legend = FALSE) +
labs(y = "Total des visiteurs",
x = "Années",
title = "Musées d'Antibes: visiteurs en hausse post-covid",
subtitle = "En milliers de visiteurs dans les musées de France",
caption = social_caption2) +
scale_x_continuous(expand = c(0, 1.3),
breaks = c(2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024)) +
scale_y_continuous(expand = c(0.1, 0.1),
labels = scales::label_number(suffix = " K")) +
ttRender
# render
animp <-
plot_musee_anim +
transition_reveal(reveal_time) +
ease_aes()
# render with timing
a <-
animate(animp,
start_pause= 5,
end_pause = 100,
fps = 25,
duration = 10,
width = 1000,
height = 1200,
res = 150 # Set resolution (DPI)
)
anim_save("posts/2025-01-25/plots/musees.gif", a)Source
Fréquentation des Musées de France de la Ville d’Antibes, disponible sur data.gouv.fr. Mise à jour du 13 janvier 2025